Python中利用Numpy分析一支股票
2018-04-09 本文已影响1065人
27efec53a72d
本博将利用Numpy分析一直股票，注意，本博不是股票交易实战，而是Numpy应用实战。全程代码实例，注释已经很详细。
股票数据源文件 AppleStock.csv 数据如下，从左到右共8列依次是：股票名称、日期、空白列、开盘价、当天最高价、当天最低价、收盘价、成交量。
AAPL,28-01-2011, ,344.17,344.4,333.53,336.1,21144800
AAPL,31-01-2011, ,335.8,340.04,334.3,339.32,13473000
AAPL,01-02-2011, ,341.3,345.65,340.98,345.03,15236800
AAPL,02-02-2011, ,344.45,345.25,343.55,344.32,9242600
AAPL,03-02-2011, ,343.8,344.24,338.55,343.44,14064100
AAPL,04-02-2011, ,343.61,346.7,343.51,346.5,11494200
AAPL,07-02-2011, ,347.89,353.25,347.64,351.88,17322100
AAPL,08-02-2011, ,353.68,355.52,352.15,355.2,13608500
AAPL,09-02-2011, ,355.19,359,354.87,358.16,17240800
AAPL,10-02-2011, ,357.39,360,348,354.54,33162400
AAPL,11-02-2011, ,354.75,357.8,353.54,356.85,13127500
AAPL,14-02-2011, ,356.79,359.48,356.71,359.18,11086200
AAPL,15-02-2011, ,359.19,359.97,357.55,359.9,10149000
AAPL,16-02-2011, ,360.8,364.9,360.5,363.13,17184100
AAPL,17-02-2011, ,357.1,360.27,356.52,358.3,18949000
AAPL,18-02-2011, ,358.21,359.5,349.52,350.56,29144500
AAPL,22-02-2011, ,342.05,345.4,337.72,338.61,31162200
AAPL,23-02-2011, ,338.77,344.64,338.61,342.62,23994700
AAPL,24-02-2011, ,344.02,345.15,338.37,342.88,17853500
AAPL,25-02-2011, ,345.29,348.43,344.8,348.16,13572000
AAPL,28-02-2011, ,351.21,355.05,351.12,353.21,14395400
AAPL,01-03-2011, ,355.47,355.72,347.68,349.31,16290300
AAPL,02-03-2011, ,349.96,354.35,348.4,352.12,21521000
AAPL,03-03-2011, ,357.2,359.79,355.92,359.56,17885200
AAPL,04-03-2011, ,360.07,360.29,357.75,360,16188000
AAPL,07-03-2011, ,361.11,361.67,351.31,355.36,19504300
AAPL,08-03-2011, ,354.91,357.4,352.25,355.76,12718000
AAPL,09-03-2011, ,354.69,354.76,350.6,352.47,16192700
AAPL,10-03-2011, ,349.69,349.77,344.9,346.67,18138800
AAPL,11-03-2011, ,345.4,352.32,345,351.99,16824200
一、计算平均价格和极值、极值波动范围
#!/usr/bin/python
# encoding:utf-8
"""
@author:jiat
@contact:cctvjiatao@163.com
@file:StockAnalysis.py
@time:2018/4/7 13:20
@description: 利用Numpy进行股票分析
"""
## 一、计算平均价格和极值、极值波动范围
import numpy as np
# 读入文件 参数1-源文件路径；参数2-分割符号；参数3-读取的列数下标（第7列是收盘价格，第8列是成交量）；参数4-是否分开放置数据
c,v = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(6,7),unpack=True)
print(c)
# result:
# [336.1 339.32 345.03 344.32 343.44 346.5 351.88 355.2 358.16 354.54
# 356.85 359.18 359.9 363.13 358.3 350.56 338.61 342.62 342.88 348.16
# 353.21 349.31 352.12 359.56 360. 355.36 355.76 352.47 346.67 351.99]
print(v)
# result:
# [21144800. 13473000. 15236800. 9242600. 14064100. 11494200. 17322100.
# 13608500. 17240800. 33162400. 13127500. 11086200. 10149000. 17184100.
# 18949000. 29144500. 31162200. 23994700. 17853500. 13572000. 14395400.
# 16290300. 21521000. 17885200. 16188000. 19504300. 12718000. 16192700.
# 18138800. 16824200.]
# 1.1、计算成交量的加权平均价格
vwap = np.average(c,weights=v)
print('vwap=',vwap)
# result: vwap= 350.5895493532009
# 1.2、计算收盘的算数平均价格
mean = np.mean(c)
print('mean=',mean)
# result: mean= 351.0376666666667
# 1.3、计算收盘的加权平均价格（时间越靠近现在权重越大）
t = np.arange(len(c))
twap = np.average(c,weights=t)
print('twap=',twap)
# result: twap= 352.4283218390804
# 1.4、寻找最大值和最小值
h,l = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(4,5),unpack=True)
print(h)
# result:
# [344.4 340.04 345.65 345.25 344.24 346.7 353.25 355.52 359. 360.
# 357.8 359.48 359.97 364.9 360.27 359.5 345.4 344.64 345.15 348.43
# 355.05 355.72 354.35 359.79 360.29 361.67 357.4 354.76 349.77 352.32]
print(l)
# result:
# [333.53 334.3 340.98 343.55 338.55 343.51 347.64 352.15 354.87 348.
# 353.54 356.71 357.55 360.5 356.52 349.52 337.72 338.61 338.37 344.8
# 351.12 347.68 348.4 355.92 357.75 351.31 352.25 350.6 344.9 345. ]
heighest = np.max(h)
print('heighest =',heighest)
# result: heighest = 364.9
lowest = np.min(l)
print('lowest =',lowest)
# result: lowest = 333.53
average = (heighest+lowest)/2
print('average = ',average)
# result: average = 349.215
# 1.5、计算最大值的波动范围和最小值的波动范围
print('Spread high price =',np.ptp(h))
# result: Spread high price = 24.859999999999957
print('Spread low price =',np.ptp(l))
# result: Spread low price = 26.970000000000027
二、对股票进行统计分析：计算中位数和方差
## 二、对股票进行统计分析：计算中位数和方差
c = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(6,),unpack=True)
print(c)
# result:
# [336.1 339.32 345.03 344.32 343.44 346.5 351.88 355.2 358.16 354.54
# 356.85 359.18 359.9 363.13 358.3 350.56 338.61 342.62 342.88 348.16
# 353.21 349.31 352.12 359.56 360. 355.36 355.76 352.47 346.67 351.99]
# 2.1 计算中位数
# 计算中位数方法一
median = np.median(c)
print('median = ',median)
# result: median = 352.055
# 计算中位数方法二：先排序，再取中间的数
sorted = np.msort(c)
print('sorted = ',sorted)
# result:
# sorted = [336.1 338.61 339.32 342.62 342.88 343.44 344.32 345.03 346.5 346.67
# 348.16 349.31 350.56 351.88 351.99 352.12 352.47 353.21 354.54 355.2
# 355.36 355.76 356.85 358.16 358.3 359.18 359.56 359.9 360. 363.13]
N = len(c)
middle = sorted[int((N-1)/2)]
print('middle = ',middle)
# result: middle = 351.99
average_middle = (sorted[int(N/2)] + sorted[int((N-1)/2)])/2
print('average_middle = ',average_middle)
# result: average_middle = 352.055
# 2.2 计算方差
# 计算方差方法一
variance = np.var(c)
print('variance = ',variance)
# result: variance = 50.126517888888884
# 计算方差方法二
variance_from_definition = np.mean((c-c.mean())**2)
print('variance_from_definition = ',variance_from_definition)
# result: variance_from_definition = 50.126517888888884
三、计算股票收益率：普通收益率和对数收益率以及收益波动率
## 三、计算股票收益率：普通收益率和对数收益率以及收益波动率
c = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(6,),unpack=True)
print(c)
# result:
# [336.1 339.32 345.03 344.32 343.44 346.5 351.88 355.2 358.16 354.54
# 356.85 359.18 359.9 363.13 358.3 350.56 338.61 342.62 342.88 348.16
# 353.21 349.31 352.12 359.56 360. 355.36 355.76 352.47 346.67 351.99]
diff = np.diff(c) # diff函数就是执行的是后一个元素减去前一个元素
print('diff =',diff)
# result:
# diff = [ 3.22 5.71 -0.71 -0.88 3.06 5.38 3.32 2.96 -3.62 2.31
# 2.33 0.72 3.23 -4.83 -7.74 -11.95 4.01 0.26 5.28 5.05
# -3.9 2.81 7.44 0.44 -4.64 0.4 -3.29 -5.8 5.32]
# 3.1、计算普通收益率
returns = diff/c[:-1]
print('returns =',returns)
# result:
# returns = [ 0.00958048 0.01682777 -0.00205779 -0.00255576 0.00890985 0.0155267
# 0.00943503 0.00833333 -0.01010721 0.00651548 0.00652935 0.00200457
# 0.00897472 -0.01330102 -0.02160201 -0.03408832 0.01184253 0.00075886
# 0.01539897 0.01450483 -0.01104159 0.00804443 0.02112916 0.00122372
# -0.01288889 0.00112562 -0.00924781 -0.0164553 0.01534601]
# 计算标准差
standard_deviation = np.std(returns)
print('standard_deviation =',standard_deviation)
# result: standard_deviation = 0.012922134436826306
# 3.2、计算对数收益率
logreturns = np.diff(np.log(c))
print('logreturns =',logreturns)
# result:
# logreturns = [ 0.00953488 0.01668775 -0.00205991 -0.00255903 0.00887039 0.01540739
# 0.0093908 0.0082988 -0.01015864 0.00649435 0.00650813 0.00200256
# 0.00893468 -0.01339027 -0.02183875 -0.03468287 0.01177296 0.00075857
# 0.01528161 0.01440064 -0.011103 0.00801225 0.02090904 0.00122297
# -0.01297267 0.00112499 -0.00929083 -0.01659219 0.01522945]
# 3.3、找出正收益率的日子
posretindices = np.where(returns>0)
print('Indices with postive returns =',posretindices)
# result:
# Indices with postive returns = (array([ 0, 1, 4, 5, 6, 7, 9, 10, 11, 12, 16, 17, 18, 19, 21, 22, 23, 25, 28], dtype=int64),)
# 3.4、计算价格年度波动率
annual_volatility = np.std(logreturns)/np.mean(logreturns)
annual_volatility = annual_volatility/np.sqrt(1./252.) # 252是一年的交易日总和
print('Annual_volatility =',annual_volatility)
# result: Annual_volatility = 129.27478991115132
# 3.5、计算价格月度波动率
monthly_volatility = annual_volatility*np.sqrt(1./12.)
print('monthly_volatility =',monthly_volatility)
# result: monthly_volatility = 37.318417377317765
四、对股票进行日期分析
## 四、对股票进行日期分析
from datetime import datetime
# Monday 0; Tuesday 1; Wednesday 2; Thursday 3; Friday 4; Saturday 5; Sunday 6
def datestr2num(s):
# return datetime.strptime(s,'%d-%m-%Y').date().weekday()
return datetime.strptime(s.decode('ascii'),'%d-%m-%Y').date().weekday()
dates, close = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(1,6),converters={1:datestr2num},unpack=True)
print('Dates =',dates)
# result:
# Dates = [4. 0. 1. 2. 3. 4. 0. 1. 2. 3. 4. 0. 1. 2. 3. 4. 1. 2. 3. 4. 0. 1. 2. 3. 4. 0. 1. 2. 3. 4.]
# 4.1、计算每周几的收盘价和平均股价
averages = np.zeros(5)
for i in range(5):
indices = np.where(dates == i)
prices = np.take(close, indices)
avg = np.mean(prices)
print('Day',i,'price',prices,'Average',avg)
averages[i] = avg
# result:
# Day 0 price [[339.32 351.88 359.18 353.21 355.36]] Average 351.7900000000001
# Day 1 price [[345.03 355.2 359.9 338.61 349.31 355.76]] Average 350.63500000000005
# Day 2 price [[344.32 358.16 363.13 342.62 352.12 352.47]] Average 352.1366666666666
# Day 3 price [[343.44 354.54 358.3 342.88 359.56 346.67]] Average 350.8983333333333
# Day 4 price [[336.1 346.5 356.85 350.56 348.16 360. 351.99]] Average 350.0228571428571
# 4.2、每周中收盘价最高的日期
top = np.max(averages)
print('Highest averages',top)
# result: Highest averages 352.1366666666666
print('Top day of the week',np.argmax(averages))
# result: Top day of the week 2
# 4.3、每周中收盘价最低的日期
bottom = np.min(averages)
print('Lowest averages',bottom)
# result: Lowest averages 350.0228571428571
print('Top day of the week',np.argmin(averages))
# result: Top day of the week 4
五、对股票进行周汇总
## 五、对股票进行周汇总
from datetime import datetime
import numpy as np
# Monday 0; Tuesday 1; Wednesday 2; Thursday 3; Friday 4; Saturday 5; Sunday 6
def datestr2num(s):
# return datetime.strptime(s,'%d-%m-%Y').date().weekday()
return datetime.strptime(s.decode('ascii'),'%d-%m-%Y').date().weekday()
dates, open, hight, low, close = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(1,3,4,5,6),converters={1:datestr2num},unpack=True)
# 数据中包含2月21日，周一，总统纪念日，节假日休市，数据的第一天是周五，也不太好处理，因此本例中暂时不考虑这两天，排除这两天，我们只考虑前三周的数据
close = close[:16] # 提取前三周数据
dates = dates[:16] # 提取前三周数据
# 找到第一个周一
first_monday = np.ravel(np.where(dates==0))[0] # where返回的是一个多维数组，所以最后要[0]
print('The first Monday index is',first_monday)
# result: The first Monday index is 1
# 找到最后一个周五
last_friday = np.ravel(np.where(dates==4))[-1]
print('The last Friday index is',last_friday)
# result: The last Friday index is 15
# 创建一个数组，组成三周里每一天的一个索引值
weeks_indices = np.arange(first_monday,last_friday+1)
print('Weeks indices initial',weeks_indices)
# result: Weeks indices initial [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]
# 编制3个数组，每个数组里面有5个元素
weeks_indices = np.split(weeks_indices,3)
print('Weeks indices after spilt',weeks_indices)
# result: Weeks indices after spilt [array([1, 2, 3, 4, 5], dtype=int64), array([ 6, 7, 8, 9, 10], dtype=int64), array([11, 12, 13, 14, 15], dtype=int64)]
def summarize(all,o,h,l,c):
monday_open = o[all[0]]
week_high = np.max(np.take(h,all))
week_low = np.min(np.take(l,all))
friday_close = c[all[-1]]
return ('Apple',monday_open,week_high,week_low,friday_close)
weeksummary = np.apply_along_axis(summarize, 1, weeks_indices, open, hight, low, close)
print('Week summary',weeksummary)
# result:
# Week summary [['Apple' '335.8' '346.7' '334.3' '346.5']
# ['Apple' '347.8' '360.0' '347.6' '356.8']
# ['Apple' '356.7' '364.9' '349.5' '350.5']]
# 保存数据到本地磁盘
np.savetxt('D:\\DataguruPyhton\\diLesson04\\stockAnalysisWeeksummary.csv',weeksummary,delimiter=',',fmt='%s')
六、计算股票的真实波动幅度均值（ATR）
## 六、计算股票的真实波动幅度均值（ATR）
N = 20 # ATR 通常取20个交易日
h, l, c = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(4,5,6),converters={1:datestr2num},unpack=True)
h = h[-N:]
l = l[-N:]
print('len(h)=',len(h),'--len(l)=',len(l))
# result: len(h)= 20 --len(l)= 20
print('Close=',c)
# result:
# Close= [336.1 339.32 345.03 344.32 343.44 346.5 351.88 355.2 358.16 354.54
# 356.85 359.18 359.9 363.13 358.3 350.56 338.61 342.62 342.88 348.16
# 353.21 349.31 352.12 359.56 360. 355.36 355.76 352.47 346.67 351.99]
previousclose = c[-N -1: -1] # 前一个交易日的收盘价
print('len(previousclose)',len(previousclose))
# result: len(previousclose) 20
print('Previous close ',previousclose)
# result: Previous close [354.54 356.85 359.18 359.9 363.13 358.3 350.56 338.61 342.62 342.88 348.16 353.21 349.31 352.12 359.56 360. 355.36 355.76 352.47 346.67]
truerange = np.maximum(h-l, h-previousclose, previousclose-l)
print('True range ',truerange)
# result:
# True range [ 4.26 2.77 2.42 5. 3.75 9.98 7.68 6.03 6.78 5.55 6.89 8.04 5.95 7.67 2.54 10.36 5.15 4.16 4.87 7.32]
atr = np.zeros(N)
atr[0] = np.mean(truerange)
for i in range(1,N):
atr[i] = (N-1)*atr[i-1]+truerange[i]
atr[i]/=N
print('ATR=',atr)
# result:
# ATR= [5.8585 5.704075 5.53987125 5.51287769 5.4247338 5.65249711
# 5.75387226 5.76767864 5.81829471 5.80487998 5.85913598 5.96817918
# 5.96727022 6.05240671 5.87678637 6.10094705 6.0533997 5.95872972
# 5.90429323 5.97507857]
七、画出股票的移动平均线：简单移动平均线和指数移动平均线
## 七、画出股票的移动平均线：简单移动平均线和指数移动平均线
# 7.1、简单移动平均线
import numpy as np
from matplotlib.pyplot import plot
from matplotlib.pyplot import show
N =5
weights = np.ones(N)/N
print('Weights =',weights)
# result: Weights = [0.2 0.2 0.2 0.2 0.2]
c = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(6,),unpack=True)
sma = np.convolve(weights,c)[N-1:-N+1]
t = np.arange(N-1, len(c))
plot(t, c[N-1:], lw=1.0)
plot(t, sma, lw=2.0)
show()
# 7.2、指数移动平均线
x = np.arange(5)
print('Exp =',np.exp(x))
# result: Exp = [ 1. 2.71828183 7.3890561 20.08553692 54.59815003]
print('Linspace =',np.linspace(-1,0,5))
# result: Linspace = [-1. -0.75 -0.5 -0.25 0. ]
N = 5
weights = np.exp(np.linspace(-1.,0.,N))
weights/=weights.sum()
print('Weights =',weights)
# result: Weights = [0.11405072 0.14644403 0.18803785 0.24144538 0.31002201]
c = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(6,),unpack=True)
ema = np.convolve(weights,c)[N-1:-N+1]
t = np.arange(N-1,len(c))
plot(t,c[N-1:],lw=1.0)
plot(t,ema,lw=2.0)
show()
sma.png
ema.png
八、画出股票的布林带
## 八、画出股票的布林带
N = 5
weights = np.ones(N)/N
print('Weights =',weights)
# result: Weights = [0.2 0.2 0.2 0.2 0.2]
c = np.loadtxt('D:\\DataguruPyhton\\diLesson04\\AppleStock.csv', delimiter=',',usecols=(6,),unpack=True)
sma = np.convolve(weights,c)[N-1:-N+1]
deviation = []
C = len(c)
for i in range(N-1,C):
if i+N < C:
dev = c[i:i+N]
else:
dev = c[-N:]
averages = np.zeros(N)
averages.fill(sma[i-N-1])
dev = dev -averages
dev = dev**2
dev = np.sqrt(np.mean(dev))
deviation.append(dev)
deviation = 2*np.array(deviation)
print(len(deviation), len(sma))
# result: 26 26
upperBB = sma + deviation
lowerBB = sma - deviation
c_slice = c[N-1:]
between_bands = np.where((c_slice<upperBB)&(c_slice>lowerBB))
print(lowerBB[between_bands])
# result:
# [329.23044409 335.70890572 318.53386282 321.90858271 327.74175968
# 331.5628136 337.94259734 343.84172744 339.99900409 336.58687297
# 333.15550418 328.64879207 323.61483771 327.25667796 334.30323599
# 335.79295948 326.55905786 324.27329493 325.47601386 332.85867025
# 341.63882551 348.75558399 348.48014357 348.01342992 343.56371701
# 341.85163786]
print(c[between_bands])
# result:
# [336.1 339.32 345.03 344.32 343.44 346.5 351.88 355.2 358.16 354.54
# 356.85 359.18 359.9 363.13 358.3 350.56 338.61 342.62 342.88 348.16
# 353.21 349.31 352.12 359.56 360. 355.36]
print(upperBB[between_bands])
# result:
# [354.05355591 351.73509428 373.93413718 374.62741729 374.33024032
# 374.9491864 372.70940266 369.73027256 375.45299591 380.85312703
# 385.78849582 387.77920793 384.58516229 374.03132204 358.88476401
# 353.33904052 363.63294214 370.19870507 372.79598614 372.08532975
# 368.04117449 361.78441601 364.63985643 365.24657008 364.54028299
# 363.04836214]
between_bands = len(np.ravel(between_bands))
print('Ratio between bands ',float(between_bands)/len(c_slice))
# result: Ratio between bands 1.0
t = np.arange(N-1,C)
plot(t, c_slice, lw=1.0)
plot(t, sma, lw=2.0)
plot(t, upperBB, lw=3.0)
plot(t, lowerBB, lw=4.0)
show()
bulindai.png
